By Marina Axelson-Fisk
This e-book provides a advisor to construction computational gene finders, and describes the state-of-the-art in computational gene discovering equipment, with a spotlight on comparative techniques. totally up to date and accelerated, this new version examines next-generation sequencing (NGS) expertise. The e-book additionally discusses conditional random fields, improving the large assurance of issues spanning chance thought, information, info conception, optimization conception and numerical research. positive factors: introduces the basic phrases and ideas within the box; discusses algorithms for single-species gene discovering, and methods to pairwise and a number of series alignments, then describes how the strengths in either components will be mixed to enhance the accuracy of gene discovering; explores the gene gains most ordinarily captured by way of a computational gene version, and explains the fundamentals of parameter education; illustrates tips to enforce a comparative gene finder; examines NGS innovations and the way to construct a genome annotation pipeline.
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Additional info for Comparative Gene Finding: Models, Algorithms and Implementation
28, 2804–2814 (2000) 114. : Identification of protein coding regions in the human genome by quadratic discriminant analysis. Proc. Natl. Acad. Sci. USA 94, 565–568 (1997) Chapter 2 Single Species Gene Finding A gene finding model usually consists of a main algorithm that serves as a kind of “umbrella” algorithm for a large number of rather complex submodels. The submodels represent various features of a gene, such as exons, introns, and splice site models. Each submodel scores the probability, or likelihood, that each given sequence region constitutes the corresponding gene feature, and then these scores are passed on up to the main algorithm.
Acad. Sci. USA 94, 565–568 (1997) Chapter 2 Single Species Gene Finding A gene finding model usually consists of a main algorithm that serves as a kind of “umbrella” algorithm for a large number of rather complex submodels. The submodels represent various features of a gene, such as exons, introns, and splice site models. Each submodel scores the probability, or likelihood, that each given sequence region constitutes the corresponding gene feature, and then these scores are passed on up to the main algorithm.
Predicting internal exons by oligonucleotide composition and discrimant analysis of spliceable open reading frames. Nucleic Acids Res. 22, 5156–5163 (1994) 98. : Has the yo-yo stopped? an assessment of human protein-coding gene number. Proteomics 4, 1712–1726 (2004) 99. : Computer methods to locate signals in nucleic acid sequences. Nucleic Acids Res. 12, 505–519 (1984) 100. : Codon preference and its use in identifying protein coding regions in long DNA sequences. Nucleic Acids Res. 10, 141–156 (1982) 101.